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use torch.testing.assertclose instead to get more details about error in cis (#35659)

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* use torch.testing.assertclose instead to get more details about error in cis

* fix

* style

* test_all

* revert for I bert

* fixes and updates

* more image processing fixes

* more image processors

* fix mamba and co

* style

* less strick

* ok I won't be strict

* skip and be done

* up
This commit is contained in:
Arthur
2025-01-24 16:55:28 +01:00
committed by GitHub
parent 72d1a4cd53
commit b912f5ee43
255 changed files with 1048 additions and 969 deletions
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34
tests/models/reformer/test_modeling_reformer.py
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@@ -1095,7 +1095,7 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_lsh_layer_forward_complex(self):
config = self._get_basic_config_and_input()
@@ -1118,7 +1118,7 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_layer_forward(self):
config = self._get_basic_config_and_input()
@@ -1136,7 +1136,7 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_layer_forward_complex(self):
config = self._get_basic_config_and_input()
@@ -1158,7 +1158,7 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_lsh_model_forward(self):
config = self._get_basic_config_and_input()
@@ -1175,7 +1175,7 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_model_forward(self):
config = self._get_basic_config_and_input()
@@ -1191,7 +1191,7 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_lm_model_forward(self):
config = self._get_basic_config_and_input()
@@ -1210,7 +1210,7 @@ class ReformerIntegrationTests(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
torch.testing.assert_close(output_slice, expected_output_slice, rtol=1e-3, atol=1e-3)
def test_local_lm_model_grad(self):
config = self._get_basic_config_and_input()
@@ -1224,7 +1224,9 @@ class ReformerIntegrationTests(unittest.TestCase):
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
self.assertTrue(torch.allclose(loss, torch.tensor(5.8019, dtype=torch.float, device=torch_device), atol=1e-3))
torch.testing.assert_close(
loss, torch.tensor(5.8019, dtype=torch.float, device=torch_device), rtol=1e-3, atol=1e-3
)
loss.backward()
# check last grads to cover all proable errors
@@ -1246,9 +1248,9 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(grad_slice_word, expected_grad_slice_word, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, atol=1e-3))
torch.testing.assert_close(grad_slice_word, expected_grad_slice_word, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, rtol=1e-3, atol=1e-3)
def test_lsh_lm_model_grad(self):
config = self._get_basic_config_and_input()
@@ -1264,7 +1266,9 @@ class ReformerIntegrationTests(unittest.TestCase):
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
self.assertTrue(torch.allclose(loss, torch.tensor(5.7854, dtype=torch.float, device=torch_device), atol=1e-3))
torch.testing.assert_close(
loss, torch.tensor(5.7854, dtype=torch.float, device=torch_device), rtol=1e-3, atol=1e-3
)
loss.backward()
# check last grads to cover all proable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
@@ -1285,9 +1289,9 @@ class ReformerIntegrationTests(unittest.TestCase):
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(grad_slice_word, expected_grad_slice_word, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, atol=1e-3))
torch.testing.assert_close(grad_slice_word, expected_grad_slice_word, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, rtol=1e-3, atol=1e-3)
@slow
def test_pretrained_generate_crime_and_punish(self):